Ehsan Jahandarpour
Outbound SalesAI/MLPublicbeginner

Outbound Sales for AI/ML at Public Company

A step-by-step playbook for implementing outbound sales at a Public Company-stage AI/ML company. This guide covers everything from initial setup and team requirements to execution, measurement, and optimization — tailored specifically for AI/ML companies with publicly accountable marketing budget tied to quarterly targets and large, specialized teams with institutional processes. Includes specific KPIs, recommended tools, common pitfalls to avoid, and expert insights from Ehsan Jahandarpour.

Timeline: 1-2 weeks

Prerequisites

  • ✓ Established product with proven product-market fit
  • ✓ Analytics infrastructure capturing key user events
  • ✓ EU AI Act compliance and model governance requirements are rapidly evolving — ensure compliance before scaling
  • ✓ CRM and email sequencing tools configured
  • ✓ At least 5 closed deals to validate ICP assumptions

Step-by-Step Guide

1

Define your ideal customer profile

Build a detailed ICP based on company size, industry, tech stack, funding stage, and pain points. The more specific, the higher your response rates. For AI/ML companies at the Public Company stage, this step is particularly important given predictable growth and shareholder value creation.

Pro tip: Analyze your last 20 closed-won deals — what do those companies have in common? In the AI/ML context, also consider: model deployment complexity.

2

Build targeted prospect lists

Use data tools to build lists of companies and decision-makers that match your ICP. Enrich with intent signals and technographic data. For AI/ML companies at the Public Company stage, this step is particularly important given predictable growth and shareholder value creation.

Pro tip: Prioritize companies showing buying signals: hiring for relevant roles, using competitor tools, or raising funding. In the AI/ML context, also consider: GPU cost management.

3

Write personalized outreach sequences

Create multi-touch sequences across email, LinkedIn, and phone. Each message should reference something specific about the prospect company. For AI/ML companies at the Public Company stage, this step is particularly important given predictable growth and shareholder value creation.

Pro tip: First email should be under 100 words. Lead with their problem, not your product. In the AI/ML context, also consider: data quality and labeling.

4

Set up sales tech stack

Implement a CRM, email sequencer, dialer, and LinkedIn automation tool. Connect everything for unified tracking and reporting. For AI/ML companies at the Public Company stage, this step is particularly important given predictable growth and shareholder value creation.

Pro tip: Start with HubSpot or Salesforce + Apollo or Outreach. Do not over-tool early. In the AI/ML context, also consider: explainability and bias concerns.

5

Execute and iterate on outreach

Launch sequences, track open/reply rates, A/B test subject lines and CTAs. Aim for 30-50% open rates and 5-10% reply rates. For AI/ML companies at the Public Company stage, this step is particularly important given predictable growth and shareholder value creation.

Pro tip: Send outbound Tuesday through Thursday, 8-10am in the prospect timezone for best response rates. In the AI/ML context, also consider: model deployment complexity.

Expected Outcomes

  • ✓ 15-25 qualified meetings booked per SDR per month targeting AI/ML
  • ✓ Email reply rate above 8% for personalized outbound sequences
  • ✓ Outbound-sourced pipeline contributing 30-50% of total pipeline
  • ✓ Average deal size 2x higher for outbound AI/ML deals vs inbound

KPIs to Track

  • ● Email reply rate
  • ● Pipeline generated
  • ● SDR-sourced revenue

Common Mistakes to Avoid

⚠Measuring activity instead of outcomes
⚠Not aligning outbound messaging with marketing

Ehsan's Growth Commentary

AI outbound sales in 2025-2026 benefits from buyer urgency — every enterprise has a "where are we on AI?" mandate from the board. The outbound approach: position yourself as the answer to an urgent question the prospect already has. "Your competitors [name 2-3] are using AI for [specific use case]. Here's what they're achieving" creates FOMO that drives responses. The AI outbound anti-pattern: leading with AI capabilities ("our model achieves 95% accuracy on X"). Enterprise buyers are overwhelmed by AI capability claims and cannot differentiate. Lead with business outcomes specific to their industry and size. "Companies your size in [industry] are reducing [specific cost] by [specific amount] using AI for [specific workflow]" converts at 3-5x the rate of capability-focused outreach. The AI outbound window: buyer interest in AI solutions is at an all-time high and will normalize within 12-18 months. Outbound sequences that would normally take 6 months to convert are converting in 2-3 months because of board-level urgency around AI adoption.

The first email should be about them, not you. Lead with a specific observation about their company or role. In AI/ML, multi-threaded outreach (contacting 3+ people at the same account) increases response rates by 50%. Follow up at least 5 times. 80% of deals require 5+ touches, but 90% of salespeople give up after 2.

EJ

Ehsan Jahandarpour

AI Growth Strategist & Fractional CMO

Forbes Top 20 Growth Hacker · TEDx Speaker · 716 Academic Citations · Ex-Microsoft · CMO at FirstWave (ASX:FCT) · Forbes Communications Council

Frequently Asked Questions

How long does it take to see results from outbound sales in AI/ML?
For AI/ML companies at the Public Company stage, expect to see early signals within 4-8 weeks and meaningful results within 3-6 months. The timeline depends on your current baseline, team capacity, and publicly accountable marketing budget tied to quarterly targets. Focus on leading indicators early and shift to lagging indicators (revenue, retention) over time.
What budget should a Public Company AI/ML company allocate to outbound sales?
At the Public Company stage with publicly accountable marketing budget tied to quarterly targets, allocate 10-20% of your growth budget to outbound sales. For AI/ML specifically, this means investing in Hugging Face and Weights & Biases and dedicating at least one team member 50%+ of their time. Start small, prove ROI, then scale investment proportionally.
What are the biggest risks of outbound sales for AI/ML companies?
The primary risks are: (1) spreading too thin across tactics instead of going deep on one, (2) not adapting the approach to AI/ML-specific dynamics like model deployment complexity, (3) measuring vanity metrics instead of business outcomes, and (4) giving up before the tactic has time to compound. Mitigate these by setting clear success criteria and committing to a 90-day minimum test period.
Can outbound sales work alongside other growth strategies?
Absolutely — and it should. outbound sales is most powerful when combined with complementary tactics. For AI/ML at Public Company, pair it with content marketing for top-of-funnel, and a strong activation flow for conversion. The key is to avoid diluting focus: master one tactic before adding another. Think of it as stacking growth loops, not running parallel experiments.
How do I measure the ROI of outbound sales in AI/ML?
Track both leading indicators (engagement, traffic, activation) and lagging indicators (pipeline, revenue, retention). For AI/ML companies, the most important metrics are CAC from this channel, conversion rate at each funnel stage, and LTV of customers acquired through outbound sales. Set up proper attribution using UTM parameters, cohort analysis, and ideally a multi-touch attribution model. Report ROI monthly to stakeholders.